Triple

T22742012
Position Surface form Disambiguated ID Type / Status
Subject Píngdōng Xiàn E562437 entity
Predicate contains P35 FINISHED
Object Fangshan Township NE NERFINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Fangshan Township | Statement: [Píngdōng Xiàn, contains, Fangshan Township]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Fangshan Township
Context triple: [Píngdōng Xiàn, contains, Fangshan Township]
  • A. Fangshan Township chosen
    Fangshan Township is a rural coastal township located in southern Taiwan’s Pingtung County, known for its agriculture and scenic seaside landscapes.
  • B. Fangshan County
    Fangshan County is a county-level administrative region under the jurisdiction of Lüliang City in Shanxi Province, China, known for its mountainous terrain and coal resources.
  • C. Su’ao Township
    Su’ao Township is a coastal town in northeastern Taiwan known for its fishing harbor, cold springs, and role as a transportation hub in Yilan County.
  • D. Fangcun Subdistrict
    Fangcun Subdistrict is an urban administrative subdivision in Guangzhou, China, serving as the central hub of government and services for Liwan District.
  • E. Gaojing Town
    Gaojing Town is an administrative town located within Baoshan District in the northern part of Shanghai, China.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69e245513a5c81908d5cb471b4fc429d completed April 17, 2026, 2:36 p.m.
NER Named-entity recognition batch_69f1797400fc8190bec26726f434f787 completed April 29, 2026, 3:22 a.m.
Created at: April 17, 2026, 3:23 p.m.